Bagging model trees for classification problems

Authors:
S. B. Kotsiantis;G. E. Tsekouras;P. E. Pintelas
Affiliations:
Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece;Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece;Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece
Venue:
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Year:
2005

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Abstract

Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees are coupled with bagging for solving classification problems. In order to apply this regression technique to classification problems, we consider the conditional class probability function and seek a model-tree approximation to it. During classification, the class whose model tree generates the greatest approximated probability value is chosen as the predicted class. We performed a comparison with other well known ensembles of decision trees, on standard benchmark datasets and the performance of the proposed technique was greater in most cases.